Savanna woody vegetation classification – now in 3-D
Article first published online: 21 MAY 2013
© 2013 International Association for Vegetation Science
Applied Vegetation Science
Volume 17, Issue 1, pages 172–184, January 2014
How to Cite
Fisher, J. T., Erasmus, B. F.N., Witkowski, E. T.F., van Aardt, J., Wessels, K. J., Asner, G. P. (2014), Savanna woody vegetation classification – now in 3-D. Applied Vegetation Science, 17: 172–184. doi: 10.1111/avsc.12048
- Issue published online: 16 DEC 2013
- Article first published online: 21 MAY 2013
- Manuscript Accepted: 6 APR 2013
- Manuscript Received: 12 DEC 2012
- Gordon and Betty Moore Foundation
- John D. and Catherine T. MacArthur Foundation
- Grantham Foundation for the Protection of the Environment
- Avatar Alliance Foundation
- W.M. Keck Foundation
- Margaret A. Cargill Foundation
- Andrew Mellon Foundation and the endowment of the Carnegie Institution for Science
- Carnegie Foundation of New York through the Global Change and Sustainability Research Institute at the University of the Witwatersrand, Johannesburg, ZA
- Carnegie Airborne Observatory;
- Object-based image classification;
- Vegetation structure;
- Woody vegetation
The co-existence of woody plants and grasses characterize savannas, with the horizontal and vertical spatial arrangement of trees creating a heterogeneous biotic environment. To understand the influence of biogeophysical drivers on the spatial patterns of 3-D structure of woody vegetation, these patterns need to be explained over large areas to capture the context. Is there a spatially explicit, ecologically meaningful way to capture the patterns and context of 3-D woody vegetation structure?
Classification development and testing sites: landscapes in Bushbuckridge Municipality, Sabi Sand Wildtuin and Kruger National Park, Mpumalanga province, north-east South Africa.
The aforementioned structural classification approach requires appropriate 3-D and spatially explicit remote sensing data. A LiDAR-based canopy height model (CHM) and volumetric pixel (voxel) data from the Carnegie Airborne Observatory Alpha system were used to create the structural classification. First, we segmented the CHM images using multi-threshold and multi-resolution image segmentation techniques, and classified the image segments into four height classes, namely shrub (1–3 m), low tree (3–6 m), high tree (6–10 m) or tall tree (>10 m). A hierarchical a priori approach was used to develop classification criteria. The following metrics were calculated for 0.25-ha grid cells based on the cover and spatial arrangement of the four height classes: canopy cover, sub-canopy cover, canopy layers, Simpson's diversity index and cohesion. Top of canopy vegetation was classified using each metric at the 0.25-ha scale, with canopy cover being the primary classification metric. Subsequently, individual layers identified within the canopy were classified using the voxel data. We use a code system for describing classes to ensure standardization between different regions; a more traditional naming system may be used in addition for interpretation.
This system provides a more comprehensive classification of the horizontal and vertical structural diversity of savannas compared to the traditional vegetation classification systems. The description of multi-layers within the canopy should allow for a sensitive change detection method. The classification can be used in many current focus areas, including habitat suitability mapping for biodiversity conservation, strategic adaptive management and monitoring land-cover change.